The present invention relates to providing digital images with reduced color moire patterns.
One type of noise found in digital camera images appears as low frequency, highly colored patterns in regions of high spatial frequency, e.g., tweed patterns in clothing. These patterns, called color moire patterns or, simply, color moire, produce large, slowly varying colored wavy patterns in an otherwise spatially busy region. Color moire patterns are also referred to as chroma aliasing patterns, or, simply, chroma aliasing.
There are numerous ways in the prior art for reducing color moire patterns in digital images. Among these are numerous patents that describe color moire pattern reduction methods using optical blur filters in digital cameras to avoid aliasing induced color moire in the first place. However, these blur filters also blur genuine spatial detail in the image that may not be recoverable by subsequent image processing methods.
Some approaches deal specifically with digital image processing methods for reducing or removing chroma noise artifacts. One class of digital camera patents discloses improvements to the color filter array (CFA) interpolation operation to reduce or eliminate high frequency chroma noise artifacts. Another class of patents teach using different pixel shapes (i.e., rectangles instead of squares) and arrangements (e.g., each row is offset by half a pixel width from the preceding row) with accompanying CFA interpolation operations to reduce or eliminate chroma noise artifacts. However, these techniques address only high frequency chroma noise, and are generally ineffective against low frequency color moire.
There is the well known technique in the open literature of taking a digital image with chroma noise artifacts, converting the image to a luminance - chrominance space, such as CIELAB, blurring the chrominance channels and then converting the image back to the original color space. This operation is a standard technique used to combat chroma noise. One liability with this approach is that there is no discrimination during the blurring step between chroma noise artifacts and genuine chroma scene detail. Consequently, sharp colored edges in the image begin to bleed color as the blurring become more aggressive. Usually, the color bleed has become unacceptable before most of the low frequency color moire is removed from the image. Also, if any subsequent image processing is performed on the image, there is the possibility of amplifying the visibility of the color bleeding. A second liability of this approach is that a small, fixed blur kernel is almost required to try to contain the problem of color bleeding. However, to address low frequency color moire, large blur kernels would be needed to achieve the desired noise cleaning.
It is an object of the present invention to remove low frequency color moire from a digital image.
It is another object of the present invention to remove low frequency color moire from a digital image by using known chromaticities from the color digital image.
It is another object of this invention to provide an improved color moire cleaned digital image using known chromaticities from the color digital image.
These objects are achieved with a method of removing color moire pattern noise having known chromaticities from a color digital image comprising:
locating the pixels having the known chromaticities in the digital image to determine the region of color moire; and
changing the chromaticities of the located pixels in accordance with the chromaticities of the located pixels in the region of color moire so that the color moire pattern noise is reduced.
The present invention overcomes the limitation of the xe2x80x9cchroma blur trickxe2x80x9d by first separating the regions of the image with color moire from the rest of the image. Color moire regions, alone, are then processed, leaving the rest of the image unaltered. Secondly, rather than performing a formal blur on chroma aliased data, resetting the chroma values to an appropriate value eliminate any migration of errors into adjacent pixels.
The features of this invention include:
1) automated operation (no user intervention is required, although the user could be given access to some algorithm parameters to control the aggressiveness of image modification), and
2) minimal computational load (convolution methods, the standard approach to this problem, are avoided).
A novel aspect of this invention is that it uses knowledge of the color filter array (CFA) spatial sampling characteristics to separate chroma aliasing from genuine scene information so that the former can be eliminated from the image.